Catherine Coolens1, Brandon Driscoll2, Caroline Chung3, Tina Shek2, Alborz Gorjizadeh2, Cynthia Ménard3, David Jaffray4. 1. Radiation Medicine Program, Princess Margaret Cancer Center and University Health Network, Toronto, Ontario, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada. Electronic address: catherine.coolens@rmp.uhn.on.ca. 2. Radiation Medicine Program, Princess Margaret Cancer Center and University Health Network, Toronto, Ontario, Canada. 3. Radiation Medicine Program, Princess Margaret Cancer Center and University Health Network, Toronto, Ontario, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada. 4. Radiation Medicine Program, Princess Margaret Cancer Center and University Health Network, Toronto, Ontario, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada.
Abstract
OBJECTIVES: Development of perfusion imaging as a biomarker requires more robust methodologies for quantification of tumor physiology that allow assessment of volumetric tumor heterogeneity over time. This study proposes a parametric method for automatically analyzing perfused tissue from volumetric dynamic contrast-enhanced (DCE) computed tomography (CT) scans and assesses whether this 4-dimensional (4D) DCE approach is more robust and accurate than conventional, region-of-interest (ROI)-based CT methods in quantifying tumor perfusion with preliminary evaluation in metastatic brain cancer. METHODS AND MATERIALS: Functional parameter reproducibility and analysis of sensitivity to imaging resolution and arterial input function were evaluated in image sets acquired from a 320-slice CT with a controlled flow phantom and patients with brain metastases, whose treatments were planned for stereotactic radiation surgery and who consented to a research ethics board-approved prospective imaging biomarker study. A voxel-based temporal dynamic analysis (TDA) methodology was used at baseline, at day 7, and at day 20 after treatment. The ability to detect changes in kinetic parameter maps in clinical data sets was investigated for both 4D TDA and conventional 2D ROI-based analysis methods. RESULTS: A total of 7 brain metastases in 3 patients were evaluated over the 3 time points. The 4D TDA method showed improved spatial efficacy and accuracy of perfusion parameters compared to ROI-based DCE analysis (P<.005), with a reproducibility error of less than 2% when tested with DCE phantom data. Clinically, changes in transfer constant from the blood plasma into the extracellular extravascular space (Ktrans) were seen when using TDA, with substantially smaller errors than the 2D method on both day 7 post radiation surgery (±13%; P<.05) and by day 20 (±12%; P<.04). Standard methods showed a decrease in Ktrans but with large uncertainty (111.6 ± 150.5) %. CONCLUSIONS: Parametric voxel-based analysis of 4D DCE CT data resulted in greater accuracy and reliability in measuring changes in perfusion CT-based kinetic metrics, which have the potential to be used as biomarkers in patients with metastatic brain cancer. Crown
OBJECTIVES: Development of perfusion imaging as a biomarker requires more robust methodologies for quantification of tumor physiology that allow assessment of volumetric tumor heterogeneity over time. This study proposes a parametric method for automatically analyzing perfused tissue from volumetric dynamic contrast-enhanced (DCE) computed tomography (CT) scans and assesses whether this 4-dimensional (4D) DCE approach is more robust and accurate than conventional, region-of-interest (ROI)-based CT methods in quantifying tumor perfusion with preliminary evaluation in metastatic brain cancer. METHODS AND MATERIALS: Functional parameter reproducibility and analysis of sensitivity to imaging resolution and arterial input function were evaluated in image sets acquired from a 320-slice CT with a controlled flow phantom and patients with brain metastases, whose treatments were planned for stereotactic radiation surgery and who consented to a research ethics board-approved prospective imaging biomarker study. A voxel-based temporal dynamic analysis (TDA) methodology was used at baseline, at day 7, and at day 20 after treatment. The ability to detect changes in kinetic parameter maps in clinical data sets was investigated for both 4D TDA and conventional 2D ROI-based analysis methods. RESULTS: A total of 7 brain metastases in 3 patients were evaluated over the 3 time points. The 4D TDA method showed improved spatial efficacy and accuracy of perfusion parameters compared to ROI-based DCE analysis (P<.005), with a reproducibility error of less than 2% when tested with DCE phantom data. Clinically, changes in transfer constant from the blood plasma into the extracellular extravascular space (Ktrans) were seen when using TDA, with substantially smaller errors than the 2D method on both day 7 post radiation surgery (±13%; P<.05) and by day 20 (±12%; P<.04). Standard methods showed a decrease in Ktrans but with large uncertainty (111.6 ± 150.5) %. CONCLUSIONS: Parametric voxel-based analysis of 4D DCE CT data resulted in greater accuracy and reliability in measuring changes in perfusion CT-based kinetic metrics, which have the potential to be used as biomarkers in patients with metastatic brain cancer. Crown
Authors: Soo Young Chae; Sangil Suh; Inseon Ryoo; Arim Park; Kyoung Jin Noh; Hackjoon Shim; Hae Young Seol Journal: Neuroradiology Date: 2017-03-24 Impact factor: 2.804
Authors: Robert H Press; Hui-Kuo G Shu; Hyunsuk Shim; James M Mountz; Brenda F Kurland; Richard L Wahl; Ella F Jones; Nola M Hylton; Elizabeth R Gerstner; Robert J Nordstrom; Lori Henderson; Karen A Kurdziel; Bhadrasain Vikram; Michael A Jacobs; Matthias Holdhoff; Edward Taylor; David A Jaffray; Lawrence H Schwartz; David A Mankoff; Paul E Kinahan; Hannah M Linden; Philippe Lambin; Thomas J Dilling; Daniel L Rubin; Lubomir Hadjiiski; John M Buatti Journal: Int J Radiat Oncol Biol Phys Date: 2018-06-30 Impact factor: 7.038
Authors: Moisés Mera Iglesias; David Aramburu Núñez; José Luis Del Olmo Claudio; Antonio López Medina; Iago Landesa-Vázquez; Francisco Salvador Gómez; Brandon Driscoll; Catherine Coolens; José L Alba Castro; Victor Muñoz Journal: Comput Math Methods Med Date: 2015-02-19 Impact factor: 2.238
Authors: Catherine Coolens; Matt N Gwilliam; Paula Alcaide-Leon; Isabella Maria de Freitas Faria; Fabio Ynoe de Moraes Journal: Cancers (Basel) Date: 2021-05-23 Impact factor: 6.639